NDLI: Least Squares and Robust Estimation of Local Image Structure

Content Provider	Springer Nature Link
Author	Weijer, Joost Van De Boomgaard, Rein Van Den
Copyright Year	2005
Abstract	Linear scale space methodology uses Gaussian probes at scale s to observe the differential structure. In observing the differential image structure through the Gaussian derivative probes at scale s we implicitly construct the Taylor series expansion of the smoothed image. The Gaussian facet model, as a generalization of the classic Haralick facet model, constructs a polynomial approximation of the unsmoothed image. The measured differential structure therefore is closer to the ‘real’ structure than the differential structure measured using Gaussian derivatives.At the points in an image where the differential structure changes abruptly (because of discontinuities in the imaging conditions, e.g. a material change, or a depth discontinuity) both the Gaussian derivatives and the Gaussian facet model diffuse the information from both sides of the discontinuity (smoothing across the edge).Robust estimators that are classically meant to deal with statistical outliers can also be used to deal with these ‘mixed model distributions’. In this paper we introduce the robust estimators of local image structure. Starting with the Gaussian facet model where we replace the quadratic error norm with a robust (Gaussian) error norm leads to a robust Gaussian facet model.We will show examples of using the robust differential structure estimators for luminance and color images, for zero and higher order differential structure. Furthermore we look at a ‘robustified’ structure tensor that forms the basis of robust orientation estimation.
Starting Page	143
Ending Page	155
Page Count	13
File Format	PDF
ISSN	09205691
Journal	International Journal of Computer Vision
Volume Number	64
Issue Number	2-3
e-ISSN	15731405
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2005-01-01
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	scale-space theory non-linear filtering color image filtering robust estimation Artificial Intelligence (incl. Robotics) Computer Imaging, Graphics and Computer Vision Image Processing Automation and Robotics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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